// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
#define EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H

namespace Eigen {

namespace internal {

    /** \internal Low-level conjugate gradient algorithm for least-square problems
  * \param mat The matrix A
  * \param rhs The right hand side vector b
  * \param x On input and initial solution, on output the computed solution.
  * \param precond A preconditioner being able to efficiently solve for an
  *                approximation of A'Ax=b (regardless of b)
  * \param iters On input the max number of iteration, on output the number of performed iterations.
  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
  */
    template <typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
    EIGEN_DONT_INLINE void least_square_conjugate_gradient(const MatrixType& mat,
                                                           const Rhs& rhs,
                                                           Dest& x,
                                                           const Preconditioner& precond,
                                                           Index& iters,
                                                           typename Dest::RealScalar& tol_error)
    {
        using std::abs;
        using std::sqrt;
        typedef typename Dest::RealScalar RealScalar;
        typedef typename Dest::Scalar Scalar;
        typedef Matrix<Scalar, Dynamic, 1> VectorType;

        RealScalar tol = tol_error;
        Index maxIters = iters;

        Index m = mat.rows(), n = mat.cols();

        VectorType residual = rhs - mat * x;
        VectorType normal_residual = mat.adjoint() * residual;

        RealScalar rhsNorm2 = (mat.adjoint() * rhs).squaredNorm();
        if (rhsNorm2 == 0)
        {
            x.setZero();
            iters = 0;
            tol_error = 0;
            return;
        }
        RealScalar threshold = tol * tol * rhsNorm2;
        RealScalar residualNorm2 = normal_residual.squaredNorm();
        if (residualNorm2 < threshold)
        {
            iters = 0;
            tol_error = sqrt(residualNorm2 / rhsNorm2);
            return;
        }

        VectorType p(n);
        p = precond.solve(normal_residual);  // initial search direction

        VectorType z(n), tmp(m);
        RealScalar absNew = numext::real(normal_residual.dot(p));  // the square of the absolute value of r scaled by invM
        Index i = 0;
        while (i < maxIters)
        {
            tmp.noalias() = mat * p;

            Scalar alpha = absNew / tmp.squaredNorm();   // the amount we travel on dir
            x += alpha * p;                              // update solution
            residual -= alpha * tmp;                     // update residual
            normal_residual = mat.adjoint() * residual;  // update residual of the normal equation

            residualNorm2 = normal_residual.squaredNorm();
            if (residualNorm2 < threshold)
                break;

            z = precond.solve(normal_residual);  // approximately solve for "A'A z = normal_residual"

            RealScalar absOld = absNew;
            absNew = numext::real(normal_residual.dot(z));  // update the absolute value of r
            RealScalar beta = absNew / absOld;              // calculate the Gram-Schmidt value used to create the new search direction
            p = z + beta * p;                               // update search direction
            i++;
        }
        tol_error = sqrt(residualNorm2 / rhsNorm2);
        iters = i;
    }

}  // namespace internal

template <typename _MatrixType, typename _Preconditioner = LeastSquareDiagonalPreconditioner<typename _MatrixType::Scalar>> class LeastSquaresConjugateGradient;

namespace internal {

    template <typename _MatrixType, typename _Preconditioner> struct traits<LeastSquaresConjugateGradient<_MatrixType, _Preconditioner>>
    {
        typedef _MatrixType MatrixType;
        typedef _Preconditioner Preconditioner;
    };

}  // namespace internal

/** \ingroup IterativeLinearSolvers_Module
  * \brief A conjugate gradient solver for sparse (or dense) least-square problems
  *
  * This class allows to solve for A x = b linear problems using an iterative conjugate gradient algorithm.
  * The matrix A can be non symmetric and rectangular, but the matrix A' A should be positive-definite to guaranty stability.
  * Otherwise, the SparseLU or SparseQR classes might be preferable.
  * The matrix A and the vectors x and b can be either dense or sparse.
  *
  * \tparam _MatrixType the type of the matrix A, can be a dense or a sparse matrix.
  * \tparam _Preconditioner the type of the preconditioner. Default is LeastSquareDiagonalPreconditioner
  *
  * \implsparsesolverconcept
  * 
  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
  * and NumTraits<Scalar>::epsilon() for the tolerance.
  * 
  * This class can be used as the direct solver classes. Here is a typical usage example:
    \code
    int m=1000000, n = 10000;
    VectorXd x(n), b(m);
    SparseMatrix<double> A(m,n);
    // fill A and b
    LeastSquaresConjugateGradient<SparseMatrix<double> > lscg;
    lscg.compute(A);
    x = lscg.solve(b);
    std::cout << "#iterations:     " << lscg.iterations() << std::endl;
    std::cout << "estimated error: " << lscg.error()      << std::endl;
    // update b, and solve again
    x = lscg.solve(b);
    \endcode
  * 
  * By default the iterations start with x=0 as an initial guess of the solution.
  * One can control the start using the solveWithGuess() method.
  * 
  * \sa class ConjugateGradient, SparseLU, SparseQR
  */
template <typename _MatrixType, typename _Preconditioner>
class LeastSquaresConjugateGradient : public IterativeSolverBase<LeastSquaresConjugateGradient<_MatrixType, _Preconditioner>>
{
    typedef IterativeSolverBase<LeastSquaresConjugateGradient> Base;
    using Base::m_error;
    using Base::m_info;
    using Base::m_isInitialized;
    using Base::m_iterations;
    using Base::matrix;

public:
    typedef _MatrixType MatrixType;
    typedef typename MatrixType::Scalar Scalar;
    typedef typename MatrixType::RealScalar RealScalar;
    typedef _Preconditioner Preconditioner;

public:
    /** Default constructor. */
    LeastSquaresConjugateGradient() : Base() {}

    /** Initialize the solver with matrix \a A for further \c Ax=b solving.
    * 
    * This constructor is a shortcut for the default constructor followed
    * by a call to compute().
    * 
    * \warning this class stores a reference to the matrix A as well as some
    * precomputed values that depend on it. Therefore, if \a A is changed
    * this class becomes invalid. Call compute() to update it with the new
    * matrix A, or modify a copy of A.
    */
    template <typename MatrixDerived> explicit LeastSquaresConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}

    ~LeastSquaresConjugateGradient() {}

    /** \internal */
    template <typename Rhs, typename Dest> void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
    {
        m_iterations = Base::maxIterations();
        m_error = Base::m_tolerance;

        internal::least_square_conjugate_gradient(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error);
        m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
    }
};

}  // end namespace Eigen

#endif  // EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
